activity
20242026
collaborators

7 papers

cs.RO2026

CrazyMARL: Decentralized Direct Motor Control Policies for Cooperative Aerial Transport of Cable-Suspended Payloads

Viktor Lorentz, Khaled Wahba, Sayantan Auddy +2

Collaborative transportation of cable-suspended payloads by teams of UAVs has the potential to enhance payload capacity, adapt to different payload shapes, and provide built-in com…

cs.RO2026

db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF

Akmaral Moldagalieva, Keisuke Okumura, Amanda Prorok +1

State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in s…

cs.RO2025

Learning Maximal Safe Sets Using Hypernetworks for MPC-based Local Trajectory Planning in Unknown Environments

Bojan Derajić, Mohamed-Khalil Bouzidi, Sebastian Bernhard +1

This paper presents a novel learning-based approach for online estimation of maximal safe sets for local trajectory planning in unknown static environments. The neural representati…

cs.RO2025

pc-dbCBS: Kinodynamic Motion Planning of Physically-Coupled Robot Teams

Khaled Wahba, Wolfgang Hönig

Motion planning problems for physically-coupled multi-robot systems in cluttered environments are challenging due to their high dimensionality. Existing methods combining sampling-…

cs.RO2025

Survey of Simulators for Aerial Robots: An Overview and In-Depth Systematic Comparisons

Cora A. Dimmig, Giuseppe Silano, Kimberly McGuire +4

Uncrewed Aerial Vehicle (UAV) research faces challenges with safety, scalability, costs, and ecological impact when conducting hardware testing. High-fidelity simulators offer a vi…

cs.RO2025

Accelerating db-A* for Kinodynamic Motion Planning Using Diffusion

Julius Franke, Akmaral Moldagalieva, Pia Hanfeld +1

We present a novel approach for generating motion primitives for kinodynamic motion planning using diffusion models. The motions generated by our approach are adapted to each probl…